Efficient threshold for volumetric segmentation
| Autor: | Daniel Costin Ebanca, Marius Brezovan, Liana Stanescu, Dumitru Dan Burdescu, Cosmin Stoica Spahiu |
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| Rok vydání: | 2015 |
| Předmět: |
Segmentation-based object categorization
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation Image texture Minimum spanning tree-based segmentation Region growing Computer vision Artificial intelligence Range segmentation business Connected-component labeling ComputingMethodologies_COMPUTERGRAPHICS Mathematics |
| Zdroj: | ICDIP |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2197443 |
| Popis: | Image segmentation plays a crucial role in effective understanding of digital images. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new method to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The advantage of using a virtual tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image. |
| Databáze: | OpenAIRE |
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